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BASIC DATA ANALYSIS USING MICROSOFT EXCEL FOR M&E WORK

The document outlines a step-by-step guide on how to perform data cleaning and analysis using Microsoft Excel, particularly for monitoring and evaluation work. It includes definitions of data and data analysis, alongside detailed procedures for handling datasets, including checks for completeness, formatting issues, and consistency analysis. The guide emphasizes the importance of maintaining ethical standards during data handling, such as not deleting surveys without thorough review and maintaining a data cleaning log.

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BASIC DATA ANALYSIS USING MICROSOFT EXCEL FOR M&E WORK. ONLINE MASTERCLASS. 26TH Oct. 2024
DEFINITIONS: Data: The 1996 Webster’s II New Riverside Dictionary Revised Edition defines data as “information, especially information organized for analysis.” Data Analysis: The process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.“Wikipedia”
HOW TO DO DATA CLEANING STEP BY STEP GUIDANCE 1. Check for complaints and feedback before you start cleaning. We usually add a complaints and feedback question at the very end, it its absence you may also see, is there anything else you want to add type of question. Go over these first, do your referrals, to protection/child protection, or complaints and feedback focal point, before you start cleaning. 2. Convert the dataset to a table (if not already a table). If your dataset includes copy pastes from multiple exports, make sure to cover all when you are converting to table. An alternative to this is to check the table afterwards and re-arrange the size of the table to cover all data. If you prefer not to work in a table format, then add Sort & Filter option.
CONTINUATION………. 3. Initial survey deletions. Note that this concerns only a few scenarios, but we do large scale deletion/clean up at the very end, not at this stage  • Delete mock data entry, meaning the test entries or those done during trainings.  • Delete surveys if you realized that the beneficiary consent was not received, or if the interviewee was a child and parent/caregiver consent was not received. 4. Do a broad review of the dataset, identify any major issues do get an idea of what needs to be done and where to start.  • Formatting (number, text, date, etc.), start by fixing the format where needed.  • Check the open ended questions, for which you will need to clean the data into options later, mark/highlight them.  • If you received your data from multiple sources, check whether there are differences in coding, such as upper lower case text, type of alphabet used, question and answer ids, etc.
CONTINUATION……….  5. Insert “N/A” for missing data/empty cells.  • You will need to decide question by question on whether empty value was allowed, or whether there was an error in survey design, and therefore mandatory/critical data is missing. If the later, you need to resort to the data collectors, or additional reference documents you have to complete this data.  6. Clean single selection questions.  • These are usually nominal data, ie your response options will be apples, oranges, bananas, etc, or dichotomous data such as male, female. Make sure they are all written in the same way. For example, if “apple” is written in different ways, such as “Apple”,“apple”,“appple”, etc. you need to edit them all to be displayed in one format.
CONTINUATION……….  7. Clean multi-selection questions.  • These are the questions, where data collectors are selecting multiple answer options. Follow the same steps as above if needed. Differently from the single selection questions, make sure that the “other” option in this case is added as a separate column.  8. Clean open ended questions.  • Decide which open ended questions you need to clean into one of the above mentioned formats, meaning, either as a single selection, or multi-selection question type, so that you can do a descriptive analysis. Note that not all open ended questions need to be cleaned in this manner.  • While analysing open ended questions, sometimes we find the answer of different questions.And sometimes data collectors add information into the first available space to convey it.
CONTINUATION………. 9. Check for consistency. • Spending this much time with the dataset will familiarize the staff who is cleaning with the participants. You will inherently start to realize that answers to certain questions may be contradicting. Make sure that you are aware of different questions, which either check one another, meaning if one answer is positive the other cannot be negative, or purposefully put in as control questions which serve the same purpose If you observe major differences in the answer patterns, you may need to decide case-by-case in the next step when deleting surveys.
CONTINUATION………. 10. Discard/delete surveys from the analysis.  • Remember that we have entered “N/A” in empty cells and tried to complete the data as much as possible. If you delete surveys pre-maturely, you will lose data, you would have taken beneficiaries’ time without reason, if you do this first, you may even miss critical complaints.  • If you see from the dataset that a survey was discontinued, you can discard them. It means the participant changed her/his mind, and it is not ethical to analyze a discontinued survey  • Check the number of main questions in the survey, excluding the ones that needs to be filled pre-survey by the enumerator, and follow up questions linked to main lines via skip logic  • Do a case by case analysis of surveys where you realized major inconsistencies at the previous step. Consult with your colleagues when unsure.
NOTE………………………..  [IMPORTANT] Never delete surveys, because certain answers were not captured though the survey which does not meet the above two criteria.  [IMPORTANT] Never delete surveys before you check for complaints. Sometimes these may be hidden in open ended questions as well.  [IMPORTANT] Always keep a data cleaning log in a separate sheet of the same excel file.Your entries in the log should not be “6 surveys deleted because of this and that reason”, but,“surveys with the unique codes, x, y, z, deleted due to this and that reason”.Whoever reviews the process, i.e. your supervisor, colleague, checking the raw data upon reading the report, must always clearly know what was done during cleaning.And should reach the same exact result if they follow your steps logged there.
THE END. ANY QUESTIONS?